To summarize the evolution of the web, by around 2004 user-generated content started to overwhelm existing ranking algorithms. This forced changes to ranking algorithms, and as volumes of data continue to grow exponentially, such ranking algorithms couldn’t keep up. Social data is again overwhelming social filters, and therefore we are entering the next wave of the web evolution. During this next wave, new solutions will target information to you based on personal relevance. It is at this point where the web becomes personal, a web that understands you and continually adapts to your every interest.

For the social web to deliver greater relevancy, it must evolve. It must become personal. To better understand this, we must look at how the web has evolved and where it needs to get to. The web started as an information portal, and the age of email, directories and search. Therefore it is fair to say Web 1.0 = Content + Commerce directory taxonomy Lacks:

1) Context

2) Social interaction

Web 2.0 gave us read write sharing on the web which ushered in user generated content and social networking. We now double what we willingly share on the Internet each year. Our search engines couldn’t cope with the overload. Social networks gave us some filtering with the billions of “What the” phenomena narrowing down our attention from trusted broadcasters giving a sentiment or approval of information signaled onto the social graph.

• Facebook:What’s on your mind?

• Twitter:What are you doing now?

• Foursquare:Where are you now?

• Instagram:What are you seeing now?

• last.fm:What are you listening to now?

Funnily enough, the more these actions happen, the lower the likelihood that your streams of information and content being consumed are of personal relevance. This is because the social graph does not account for the fact we are constantly shifting and evolving individuals whose focus and interest change with real-time.

Web 2.0 is the transformation from directory taxonomy to a social folksonomy

Web 2.0 = Content + Commerce + Community

Web 2.0 has seen the rise of Social Media

• Leverages the social graph to allow user’s to share content and communicate

• The social graph provides viral opportunities

• This gives each user their own brand & reputation

• Unbelievable experiences and value has been unlocked

WEB 2.0 platforms are brilliant in facilitating content generation but it is highly inefficient in personalising my social media experience, lack portability and interoperability.

As the Web grows with more information, social media, platforms, real-time, connections, mobility we get FLOODED WITH INFORMATION.

MORE DISTRACTIONS = LESS ATTENTION

To truly harness the power of the social graph, we need more than a platform that connects us with everyone we know......

We need

Reduction in noise

Prioritisation of content

Individualisation of content

Implicit learning models (behaviour)

Context Serendipity discovery

As Eric Schmidt quoted “The power of individual targeting — the technology will be so good it will be very hard for people to watch or consume something that has not in some sense been tailored for them."

“The next Web is not a separate Web, but an extension of the current one, in which information is given well-defined meaning and context”

• Personalised Web my view of the web shaped for me focused on the individual

• Contextual Discovery information organisation

•“me-onomy” the context is me!

• Autonomous Search machine surfing

• Pull business models

No longer one-way push by brands but by connected customers voting in the invitation for brands to connect with them.

What are the properties of a personalized Web;

• Mobile

• Influence

• Connecting business to clients dynamically

• Personalization

• Bridging online and offline interactions

• Social Discovery

• Connecting people through the Interest Graph

Context advances Web 2.0 towards the next web, a personalised Web. When machines can understand the context of content, and of a user, the web can better satisfy a user’s needs Context helps enable personalisation, but there is more needed……..

The interest graph provides a new way to discover content & people with similar tastes to yours. It is the new way to get from both within, and beyond, your social graph, the content that will interest you personally It forms a network of people who share interests with you, but who you don’t necessarily know a connection within the social graph.

The interest graph is an online representation of individuals’ interests. Combined with context, it allows us to float relevant content in front of a user’s attention. Some people consider this the middle ground between search, advertising and the social graph .

Gorillas are vying for position.

“The notion of autonomous search – to tell me things I didn’t know but am probably interested in – is the next great stage of search”

Eric Schmidt, Google

“Google search will continue to become more personalised. “ He noted that thousands of Google engineers are currently working on beefing up search with artificial intelligence in hopes to find us the results we want right away.

Eric Schmidt, Google 28th Feb 2012 Barcelona

“With 100 million tweets flowing through the system on a daily basis, there’s something for everyone, but the real challenge is finding the most valuable stuff for you”

Evan Williams, Twitter

“With a normal website, your technology is focused on caching. But Facebook is completely personalized. Every time you visit, you get a unique personal experience”

Bret Taylor, Facebook

The interest graph introduces a new challenge and opportunity for brands to communicate their messages to those people that will genuinely be interested in what they have to say

The interest graph promises to deliver marketers messages to those that are, or should be interested

Web personalisation puts the user in the centre. It converges the broadcast centric social graph with the interest graph and both explicit and implicit behaviour to give the user highly personalised discovery, curation, transaction, communication and socialising functions with both information and people of common and similar interests.

A Personalised web will be very relevant, and very dynamic. Marketeers will need to create content that can travel through the interest graph. The intelligence must be embedded, or discoverable within the content and with context and advanced metadata. Sites and applications must deliver on-demand, interest focused content for socialisation on the web. How this content moves through the web will be fundamentally outside of content providers control.

By directing people’s attention provides many opportunities, particularly given attention is truly our most valued currency. Once the web knows your interests, the way in which we consume the web can start to change. Information that is not discoverable through keyword search starts to float to the top of your interest streams. New people and information can be discovered, rather than pushed at us. Or more precisely, information is pulled to the surface to the huge corpus of information for each user. This will not only drastically improve information discovery, but it will accelerate collaboration and conversation and focus our attention on what matters most.

With a personalised view of the web, each and every user will be connected with the right information at the right time.

Further, by accessing our technology through defined interfaces, any website or application can use knowledge of a user’s interests in order to give them a personal experience.

The potential of harnessing this data will become invaluable for brands, so marketers will be using the power of the data in the near future to their advantage. When brands create content, they will also need to define the context that links the content to the interest graph.

Marketers will have the ability to take this personalised data to drill down to find their core customer accurately.

The evolution to a personalised web is not without its challenges.

1. Web-scale interest ranking engines

2. Artificial intelligence

3. Machine learning

4. Content categorisation

5. Linking it all together

Context Discovery platform used within web applications allows each user the flexibility to immerse themselves into their own “slice of the stream” by filtering for relevance. And by conversing in the stream, our platform allows a user to subscribe to signals reflecting what their friends, colleagues, and like-minded people are finding relevant.

The signals don’t stop there. Context Discovery platforms will evolve further harvesting other web signals and further specialise from the monolithic Interest Graph into Taste Graphs, Financial Graphs, Psychometric graphs, Local Network Graphs, Transaction Graphs..think Square.com etc. through implicit graph definitions. Think of the psychometric data we could add or harvest. This signal could be used to find affinities or the best collaborative mix to organically setup task force teams in the social enterprise. This technology has already begun.

. Imagine the potential for structured innovation solutions either for social networking around interests or business agility. To quickly deploy available human resources with skill, psychometric recommended highly comaptible membership linked into a task force that organically fuses around a customer opportunity. The team creates solutions, integrates into processes and the social enterprise holistic system and is ready for the next task. Organic teams, valuable, holistic and visible, and rewarded in social enterprise eco system. If I were a CEO transforming to this new attention/digital economy I’m sure I would see that as a valuable capability. However that’s another topic.

Artificial Intelligence: Former Siri Makers Designing an AI That Does Everything Latin Post Viv as an A.I. is growing, but the interest of A.I. technology is getting digital international attention. Google recently ...

I can see where this is going. It's the hub spot in the home connecting and deep learning from our personalised web of social location mobile interactions and also from our home of things, and our quantified self and perhaps " the quantified family". I can see this evolving to perhaps more a play for Google, Samsung, Amazon, Facebook or Apple Eco systems where it might be the tv that's well positioned as your intelligent home robot deep learning from all the connected data, and Siri speaking to you with Dr Phil app making suggestions to the family dynamics and Jamie Oliver app making weekly meal and grocery suggestion to improve dietary performance. There may also be a ad recommendation brought to the surface for your attention for home insulation consultants as the robot detects your home thermal properties ( Smartthings) are inadequate compared to neighbors homes around you. An alarm when Pluto the family dog leaves the home perimeter. An automated request for pricing to energy companies on your grid for next quarters energy based on your homes consumption profile. The team at Indiegogo look like the right team to deliver on their initial idea but I'd imagine there will be a quick exit down the road with an eco player like Apple , Samsung or Google. Ultimately the winning central intelligent hub spot and buddy to the home will lie in its algorithmic ability to understand relationships and patterns in interactions delivering knowledge that comes from the strategic assets that enable learning from increasingly more connections ( people and things) to increase context and relevance against population based understanding. I think the long term impact of this concept in our homes could be quite profound in the way it might shape our attitudes, behaviors and environment. Big for consumerism, and for better living. An algorithmic driven world. It starts to sound a little frightening.

This article is helpful in thinking through the structural trends of the economy, particularly as digital disruption removes the friction between modules of work processes and enables transparency of the information connecting these modules. That in turn enables flexibility, adaptability. The concept leads to two powerful drivers. One, Optimising costs as these modules link towards others that are assessed on how cost efficiently it can perform. Second, Innovation as modules of work assemble to compete from the new afforded potential of increased connectedness to other modules. This allows exponential expansion to increase understanding and create new knowkedge. Ideation on steroids but on top of a modular economy that also delivers a darwinian cost optimised system.

Why is it that so many traditional companies with an enormous wealth of assets largely fail to transform them for the digital era? By assets here, I mean established customer base, closely held rel...

matthew kapp's insight:

Great article. Such a dilemma for traditional enterprises in markets faced with digital disruption. The 1st phase of the internet was business driven to the consumer. Now collaboration through web and social media enables another phase to exploit assets digitally. This 2nd phase of the Internet is also where the consumer is driving back into business, sharing ideas and opinions virally. The new capabilities to open up the porosity for conversation and collaboration, embracing new agile networked eco systems, won’t arise under the same leadership cultures that created the business process structures that were so successful traditionally, building and pushing products to markets.. Enterprises must now excel in the organisational properties needed for this digital era that allow agile organic structures that interact, adapt and share with community and partnership, enabling collaboration and innovation at a speed and over such a scale, never before possible. This is particularly true as their market demands mass customisation and personalised engagement. I agree that the key issue for traditional enterprise organisations is where the “ Business leaders and BOD that can’t deeply see the way forward for their organizations”. It’s very difficult for them to realize they are the friction point for transformation. It’s then even more difficult to lead the transformation of the required culture if your traditional hard wired trained skills and thinking is so different.

Contextual Computing space moves us towards the applications that predict what you want and autonomoulsy finds the resources and people that can help deliver what you want …A.I. Personal Digital Assistant.

As predicted knowledge and reputation graphs will relate and rank what's most evident to be true. I'd imagined one day soon artificially intelligent wikipedia like application giving us truth news, a curated news site for reasonable people. Applications are endless such as healthcare facts etc.

Finding the one true algorithm of perceptual learning, in particular the branch of AI called deep learning, is the key goal of Baidu’s new Silicon Valley AI lab in Sunnyvale, run by Stanford machine learning researcher Adam Coates.

Workforce science and the interest graph."the future of human resource acquisition and development, through the use of smart technologies that help predict prospective employees’ behavior and their integration into the work community – something resumes can’t always do. These game-changers are helping organizations embrace talent wherever it surges, transforming the way companies recruit and source the workforce."

Artificial intelligence is still in the very early stages of development–in so many ways, it can’t match our own intelligence–and computers certainly can’t replace doctors at the bedside.

matthew kapp's insight:

" Tech titans like Google, Amazon, Microsoft, and Apple already have made huge investments in artificial intelligence to deliver tailored search results and build virtual personal assistants. Now, that approach is starting to trickle down into health care, thanks in part to the push under the health reform law to leverage new technologies to improve outcomes and reduce costs–and to the availability of cheaper and more powerful computers. In an effort to better treat their patients, doctors are now exploring the use of everything from IBM’s Watson machine learning supercomputer, the machine that won at Jeopardy, to iPhone-like pop-up notifications that appear in your online medical records.

Personalisation maybe the holy grail between search and advertising but it's a concern that the services ROI model re enforces a filter bubble narrowing the users world and other interests, sentiments and perspective.

Computers are already smart, just in their own ways. They catalogue the breadth of human knowledge, find meaning in mushroom clouds of data, and fly spacecraft to other worlds. And they're getting better.

matthew kapp's insight:

Next generation Medical Research platforms will layer patient networks and networks of clinical trials, interest, environment, and other knowledge to infer new information and support collaboration allowing scientist to discover new knowledge at exponential rate. It can link and intelligently source from many of the growing knowledge bases such as biobank and genome databases, adaptive trial learnings, or the Pharmacogenomics Knowledge Base (PharmaGKB) which has read 26 million scientific abstracts to create a searchable index of different effects that various drugs have on individual genes. The program understands things like clauses and how the meaning of a word can be modified by the words around it (which is important for parsing dense phrasing that might send a confusing message about whether a drug activates a gene), and also knows many synonyms and antonyms. The resulting database is hugely important to pharmaceutical companies, who use it to save time and money on basic research when they are searching for new drug combinations.

Australian company bosses have an unprecedented responsibility to face up to so-called “digital disruption” caused by new technology or face potential extinction, a senior RBA official says.

matthew kapp's insight:

Traditional technically challenged industries are being blind-sided by newer, much savvier, tech-centric, network-oriented new digital businesses. Can traditional firms shift and lead these new competitive effects or are their cultures inherently designed to perpetuate the problem they were invented to solve which ensures that a company is unlikely to aggressively re-invent itself until it’s in the process of being disrupted? A tech savvy culture and leadership is needed to operate in an ecosystem of self organising employees, networked partnerships and customer interaction moving beyond mechanised pushed selling strategies to attraction strategies that open communities and partnerships that work on top of their digital platforms.

With the pace of technological development accelerating at an exponential rate, life could drastically change in a shorter time than expected. In a few years time some of the technologies listed below will become commonplace even though some of us do not even know that the technology exists. Take a look at the list below to get a glimpse into the future.

Facebook is ripping chat out of its flagship mobile app. That's going to be a big pain for users, but it's a signal that Facebook is ready for its app-centric future.

matthew kapp's insight:

While users may grow attached to services that work the way they’re used to, like the full-featured Facebook app, the growing Silicon Valley consensus is that people really want a more bite-sized future

If data is currency, today's tech companies getting rich off our every online move. Data privacy company Personal, however, wants to flip that around. The company, which creates a digital vault for...

matthew kapp's insight:

Personal is a company that puts people in control of their data to collect, track and barter their personal data for relevant products and services, cost savings, convenience and customisation. This is "the power of pull". Imagine people grouping their data into coops to barter even further. That's a switch to consumer power but will software develop to help people self organise.

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